
'''simple docstring'''






def 		A_							(						_lowerCAmelCase :     list[int]					,						_lowerCAmelCase :     list[int]					,						_lowerCAmelCase :     int				):



      """simple docstring"""




      return not any(
          neighbour == 1 and colored_vertices[i] == color
          for i, neighbour in enumerate(_lowerCAmelCase				)				)







def 		A_							(						_lowerCAmelCase :     list[list[int]]					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     list[int]					,						_lowerCAmelCase :     int				):



      """simple docstring"""




      if index == len(_lowerCAmelCase				):
            return True

      # Recursive Step
      for i in range(_lowerCAmelCase				):
            if valid_coloring(graph[index]					,						_lowerCAmelCase					,						_lowerCAmelCase				):
                  # Color current vertex
                  _lowerCamelCase					: Dict		    = i
                  # Validate coloring
                  if util_color(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						index + 1				):
                        return True
                  # Backtrack
                  _lowerCamelCase					: List[Any]		    = -1
      return False







def 		A_							(						_lowerCAmelCase :     list[list[int]]					,						_lowerCAmelCase :     int				):



      """simple docstring"""




      _lowerCamelCase					: Optional[int]		    = [-1] * len(_lowerCAmelCase				)

      if util_color(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						0				):
            return colored_vertices

      return []

"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch



@require_torch
@require_optimum
@slow
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer('''This is me'''      ,return_tensors='''pt'''			)

				lowerCAmelCase												=  model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				lowerCAmelCase												=  model.generate(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

					self.assertFalse(
					    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

					lowerCAmelCase												=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
						model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model.reverse_bettertransformer()
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


